Artificial intelligence for sustainable environmental management in the mining sector: A review

Importance: The mining industry must balance global resource demand with the urgent need to reduce environmental impacts such as air pollution, water contamination, soil degradation, and greenhouse gas emissions. Artificial Intelligence (AI) offers powerful tools to support sustainable practices by enabling predictive analytics, monitoring, and optimization. Research Gap: While AI’s potential for sustainability is recognized, existing research rarely provides systematic analysis of its specific applications in mining. Gaps remain in evaluating performance benchmarks, addressing integration challenges, and considering ethical and regulatory issues. Objective: This review examines AI applications in mining with a focus on their role in mitigating environmental impacts, identifying both opportunities and limitations in advancing sustainable operations. Methodology: The study synthesizes peer-reviewed literature and case studies, covering AI use in air quality monitoring, water resource management, soil restoration, tailings stability, energy optimization, digital twins, and ecosystem modelling. Key Findings: AI systems have achieved notable results, including >90% accuracy in slope stability prediction, 25% reduction in wastewater treatment costs, and 8–12% fuel savings through reinforcement learning. Persistent barriers include data scarcity, high computational energy demands, integration with legacy systems, and limited interpretability of deep learning models. Implications: This review highlights AI’s potential to significantly reduce the environmental footprint of mining if implemented responsibly. Approaches such as explainable AI, federated learning, and energy-efficient frameworks are essential to ensure transparency, scalability, and sustainable long-term adoption